Method

DetNosNa [DFR]
[Anonymous Submission]

Submitted on 10 Nov. 2023 15:44 by
[Anonymous Submission]

Running time:0.01 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
Anonymous
Parameters:
Anonymous
Latex Bibtex:
Anonymous

Detailed Results

From all 29 test sequences, our benchmark computes the HOTA tracking metrics (HOTA, DetA, AssA, DetRe, DetPr, AssRe, AssPr, LocA) [1] as well as the CLEARMOT, MT/PT/ML, identity switches, and fragmentation [2,3] metrics. The tables below show all of these metrics.


Benchmark HOTA DetA AssA DetRe DetPr AssRe AssPr LocA
CAR 81.71 % 78.61 % 85.53 % 84.76 % 84.37 % 89.42 % 89.96 % 87.79 %

Benchmark TP FP FN
CAR 32921 1471 1628

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 90.96 % 86.51 % 90.99 % 10 78.04 %

Benchmark MT rate PT rate ML rate FRAG
CAR 86.92 % 7.38 % 5.69 % 76

Benchmark # Dets # Tracks
CAR 34549 650

This table as LaTeX

This figure as: png pdf

[1] J. Luiten, A. Os̆ep, P. Dendorfer, P. Torr, A. Geiger, L. Leal-Taixé, B. Leibe: HOTA: A Higher Order Metric for Evaluating Multi-object Tracking. IJCV 2020.
[2] K. Bernardin, R. Stiefelhagen: Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics. JIVP 2008.
[3] Y. Li, C. Huang, R. Nevatia: Learning to associate: HybridBoosted multi-target tracker for crowded scene. CVPR 2009.


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